Predicting customer churn with the purpose of retaining customers is a hot topic in academy
as well as in today’s business environment. Targeting the right customers for a specific …

Decision trees and logistic regression are two very popular algorithms in customer churn
prediction with strong predictive performance and good comprehensibility. Despite these …

The popularity of the big data domain has boosted corporate interest in collecting and storing
tremendous amounts of consumers’ textual information. However, decision makers are …

Business-to-business (B2B) customer retention relies heavily on analytics and predictive
modeling to support decision making. Given this, we introduce uplift modeling as a relevant …

CRM gains increasing importance due to intensive competition and saturated markets. With
the purpose of retaining customers, academics as well as practitioners find it crucial to build …

The increased availability of social media big data has created a unique challenge for marketing
decision-makers; turning this data into useful information. One of the significant areas of …

Data preparation is a process that aims to convert independent (categorical and continuous)
variables into a form appropriate for further analysis. We examine data-preparation …

Customer complaint management is becoming a critical key success factor in today's
business environment. This study introduces a methodology to improve complaint-handling …

We studied the problem of optimizing the performance of a DSS for churn prediction. In
particular, we investigated the beneficial effect of adding the voice of customers through call …

The online gambling industry is one of the most revenue generating branches of the
entertainment business, resulting in fierce competition and saturated markets. Therefore it is …